With the successful scale-up of HIV treatment in hyper-endemic settings, and a disease prevalence that may remain high for decades, comes a long-term healthcare commitment to an ageing HIV-infected population. As HIV-infected individuals live longer they will increasingly be affected by a double burden of both infectious and non-infectious diseases. We hypothesize that the current HIV-specific vertical programs are unlikely to maximize the total health of the HIV-positive individual and that in the coming years more integrated health care management - incorporating testing and treatment for diabetes, cancer, renal insufficiency, cardiovascular disease and hepatitis, among other conditions - will be needed both to stop these diseases from undermining HIV programs and to maintain and improve the reductions in mortality that have begun in hyper-endemic settings. We know this shift in disease burden is coming, but we do not know its magnitude, timing or character in detail; nor are the implications of this shift well understood; no work has established the priorit that should be placed on potential changes in health system organization. The only tool available to examine these issues at a population-scale is mathematical modelling. Therefore, we propose to construct a new mathematical model for Zimbabwe, using data from a long-running population-based HIV cohort, to develop innovative translational analyses to inform the evolution the AIDS response must take. Our aims are: 1. To construct a multiple-disease model tailored specifically to the HIV hyper-endemic African setting of Zimbabwe. The model will build on existing analyses of demographic, epidemic and program changes occurring in Zimbabwe, incorporate interactions between HIV and ART with major non-communicable diseases in this population - diabetes, cancer, cardiovascular disease, kidney disease, osteoporosis and mental health issues - and major infectious diseases - tuberculosis, hepatitis B and C, herpes simplex virus, human papillomavirus, malaria, schistosomiasis. The model will also represent patterns of care-seeking behaviors and health system organization (screening different population, outreach to different group, integrating care across sectors to improve 'cascade' losses). 2. To use this model to forecast the changing disease burden in HIV-infected patients and the wider population. Specifically, the timing and scale of change and characterizing future patient profile (sex/age/multi-morbidities). 3. Identify optimal mode of integration of services across health areas. What screening and additional services will be required, for which patients; which demographics should outreach campaigns prioritize; where would integration of services and strengthening of the cascade of care be most valuable; what impact would these changes have on population health.